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近端学习用于外部控制试验：HIV预防的案例研究

Proximal Learning for Trials With External Controls: A Case Study in HIV Prevention

Columbia University（哥伦比亚大学）；University of Washington（华盛顿大学）；David Geffen School of Medicine, University of California, Los Angeles（加州大学洛杉矶分校大卫格芬医学院）；Bridge HIV, San Francisco Department of Public Health（旧金山公共卫生局桥接艾滋病项目）；Emory University（埃默里大学）；National Institute of Allergy and Infectious Diseases（美国国家过敏和传染病研究所）；St. Jude Children’s Research Hospital（圣裘德儿童研究医院）；University of Michigan（密歇根大学）

Yilin Song, Yinxiang Wu, Raphael J. Landovitz, Susan Buchbinder, Srilatha Edupuganti, Lydia Soto-Torres, Kendrick Li, Xu Shi, Fei Gao, Deborah Donnell, Holly Janes, Ting Ye

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AI总结：

本文提出近端因果推断方法，用于估计主动控制试验中安慰剂的HIV病例数，通过外部控制数据验证卡博特格韦的疗效。

摘要：

随着有效预防暴露 prophylaxis 药物的出现，主动控制 HIV 预防试验已成为常见的研究设计。然而，估计相对于安慰剂的绝对疗效仍然很重要。在本文中，我们介绍了一种新颖的近端因果推断方法应用，用于估计在主动控制试验中使用卡博特格韦的受试者在安慰剂控制试验中的反事实累积 HIV 病例数，利用具有相似纳入标准的安慰剂控制试验的外部控制数据。我们利用基线性传播感染状况和地理区域作为负控制结果和暴露变量。我们解决了两个关键挑战：试验之间未测量的 HIV 风险差异以及来自两项研究的低 HIV 病例率带来的统计困难。为克服这些挑战，我们开发了两种近端推断方法：（1）半参数逆概率删失加权估计量，以及（2）针对低事件率设置的两阶段回归策略。我们的理论和数值研究证明这些方法能够可靠地估计安慰剂下的反事实一年累积 HIV 病例数，并提供强有力的证据，证明卡博特格韦相对于安慰剂的优越疗效。这些发现突显了近端推断方法在利用外部控制数据估计单臂和主动控制试验中的安慰剂效应潜力。

英文摘要：

With the advent of effective pre-exposure prophylaxis agents, active-controlled HIV prevention trials have become a common study design. Nevertheless, estimating absolute efficacy relative to a placebo remains important. In this paper, we introduce a novel application of proximal causal inference methods to estimate the counterfactual cumulative HIV incidence under placebo for participants in an active-controlled trial of cabotegravir, using external control data from a placebo-controlled trial with similar eligibility criteria. We leverage baseline sexually transmitted infection status and geographic region as negative control outcome and exposure variables, respectively. We address two key challenges: unmeasured differences in HIV risk between trials and statistical difficulties arising from low HIV incidence rates in both studies. To overcome these challenges, we develop two proximal inference approaches: (1) a semiparametric inverse probability of censoring weighting estimator, and (2) a two-stage regression-based strategy tailored to low-event-rate settings. Our theoretical and numerical investigations demonstrate these methods yield reliable estimates of the counterfactual one-year cumulative HIV incidence under placebo, and provide robust evidence of the superior efficacy of cabotegravir compared with placebo. These findings highlight the potential of proximal inference methods to estimate placebo-controlled effects in both single-arm and active-controlled trials by leveraging external controls.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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